A.4 Neurological events following COVID-19 vaccination: does ethnicity matter?
Bibliographic record
Abstract
Background: Neurological complications following vaccinations have been described before, but the rates of neurological complications, and their variation by ethnicity, following COVID-19 vaccine are not well-known. Methods: We conducted a population-based cohort study of Ontarians aged 18 years and over who received their first COVID-19 vaccine, and followed them for six weeks to estimate the incidence of neurological events, ascertained using validated case definitions based on ICD-10 codes. Ethnicity was defined using last name surname algorithm. We used multivariable logistic regression models, adjusting for age, sex, and vaccine-type to evaluate ethnic differences. Results: In the included 10,063,466 Ontario residents, incidence of GBS (n=72), CVST (n=52) and transverse myelitis (n=25) after first COVID-19 vaccine was rare. The crude rate of ischemic stroke (240/1,000,000 people) was the highest followed by Bell’s palsy (54/1,000,000). Compared to the general population, the adjusted odds of ischemic stroke and Bell’s palsy were lower in Chinese (aOR Bell’s 0.62; 0.39-0.98 and a OR ischemic stroke 0.74; 0.59-0.91) and South Asians (aOR Bell’s 0.83; 0.52-1.31 and aOR ischemic stroke 0.84; 0.65-1.08). Conclusions: The incidence of neurological events following COVID-19 vaccine is low, and it varies by ethnicity. Our findings should encourage vaccination against COVID-19 in all ethnic groups.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".